Method and system for monitoring a patient for atrial fibrillation and/or asystole

ABSTRACT

Methods and systems methods for continuously monitoring a patient for cardiac electrical abnormalities including atrial fibrillation, asystole, ventricular fibrillation and tachycardia.

The present application claims benefit of U.S. Provisional ApplicationNo. 62/735,793, filed Sep. 24, 2018, which is hereby incorporated byreference in its entirety including all tables, figures, and claims andfrom which priority is claimed.

BACKGROUND OF THE INVENTION

Atrial fibrillation (AF) is a supraventricular tachyarrhythmia withuncoordinated atrial activation and consequently ineffective atrialcontraction. Characteristics on an electrocardiogram (ECG) include 1)irregular R-R intervals (when atrioventricular (AV) conduction ispresent), 2) absence of distinct repeating P waves, and 3) irregularatrial activity. AF may be triggered by potentially reversible, oracute, causes such as surgery (cardiac and noncardiac), hyperthyroidism,myocarditis or pericarditis, myocardial infarction, pulmonary embolism,pneumonia, and alcohol intoxication.

While loop recorders, pacemakers, and defibrillators offer thepossibility of reporting frequency, rate, and duration of abnormalatrial rhythms, including AF, a challenge to the use of body-worn AFmonitors for continuous monitoring and reporting of AF arises from noiseand artifacts prevalent in ambulatory monitors, resulting in falsealarms, irrelevant data that is incorrectly identified for analysis, anda resulting alarm fatigue.

SUMMARY OF THE INVENTION

This invention provides a method and body-worn system for continuouslymonitoring a patient for cardiac electrical abnormalities includingventricular fibrillation/tachycardia, AF and/or asystole.

In a first aspect, the invention relates to methods for continuouslymonitoring a patient for cardiac electrical abnormalities, comprising:

-   obtaining a plurality of time-dependent electrocardiogram (ECG)    waveforms from an ECG sensor comprising plurality of ECG electrodes,    each waveform in the plurality of waveforms corresponding to    electrical signals obtained from one ECG electrode in the plurality    of ECG electrodes;-   processing the plurality of waveforms by    -   determining a time-dependent first signal quality parameter for        each waveform in the plurality of waveforms and curating the        plurality of waveforms by comparing each first signal quality        parameter to a first quality threshold metric,    -   wherein if at least one first signal quality parameter exceeds        the first quality threshold metric, accepting those waveforms        having a first signal quality parameter that exceed the first        quality threshold metric and discarding those waveforms having a        first signal quality parameter that does not exceed the first        quality threshold metric, or if no first signal quality        parameter exceeds the first quality threshold metric, accepting        all waveforms, and

combining the accepted waveforms to provide a time-dependent combinedECG waveform;

-   processing the combined ECG waveform to by    -   identifying each QRS complex in the combined ECG waveform,    -   determining a second signal quality parameter for each QRS        complex by gravity cliff detection, and    -   curating each second signal quality parameter by comparing each        second signal quality parameter to a second quality threshold        metric, wherein if the second signal quality parameter exceeds        the second quality threshold metric, the QRS complex is        identified as a valid QRS complex;-   determining the occurrence or nonoccurrence of asystole and/or    atrial fibrillation from the valid QRS complexes; and-   causing an alarm to be displayed on a display component when    asystole and/or atrial fibrillation is determined to occur.

In certain embodiments, the accepted waveforms are combined to provide atime-dependent combined ECG waveform by averaging of the acceptedwaveforms.

In certain embodiments the method comprises determining the occurrenceor nonoccurrence of asystole, wherein asystole is determined to occurwhen no valid QRS complexes are identified over a predetermined timeperiod.

In still other embodiments, the method comprises determining theoccurrence or nonoccurrence of atrial fibrillation, wherein atrialfibrillation is determined by, for a plurality of pairs of consecutivevalid QRS complexes occurring over a predetermined time period,

-   -   for each consecutive pair of valid QRS complexes, determining an        interval between a first fiducial point in the first member of        the consecutive pair to a corresponding fiducial point in the        first member of the consecutive pair, thereby providing a        plurality of intervals,    -   curating the plurality of intervals by calculating a third        signal quality parameter for each interval and comparing each        third signal quality parameter to a third quality threshold        metric, wherein if the third signal quality parameter exceeds        the third quality threshold metric, the interval is identified        as a valid interval, and    -   classifying whether the valid intervals obtained from the        plurality of pairs of consecutive valid QRS complexes are        indicative of atrial fibrillation.

In various embodiments, the classifying step comprises calculating aroot mean square of successive differences in the valid intervals;calculating a sample entropy of successive differences in the validintervals; or both. By way of example, the classifying step may comprisecalculating a two dimensional space that is a function of a root meansquare of successive differences in the valid intervals and a sampleentropy of successive differences in the valid intervals, and definingvalues that fall within an area or multiple areas within the twodimensional space as being indicative of the occurrence of atrialfibrillation.

In certain embodiments, the method further comprises determining theoccurrence or nonoccurrence of ventricular fibrillation/tachycardia.Such determination may comprise the following steps:

-   processing at least two of the plurality of time-dependent ECG    waveforms by    -   selecting from each of the at least two ECG waveforms, a first        waveform segment of time length t, and a second waveform segment        of time length t, wherein the first and second waveform segments        are non-overlapping consecutive segments, and    -   for each of the first and second waveform segments, calculating        a four-dimensional feature space comprising at least one        temporal feature, at least one spectral feature, and at least        one a complexity feature;-   for each of the at least two ECG waveforms, determining if the    four-dimensional feature space is indicative of the occurrence of    ventricular fibrillation/tachycardia, wherein if ventricular    fibrillation/tachycardia is indicated by processing of each of the    at least two ECG waveforms, the occurrence ventricular    fibrillation/tachycardia is determined; and-   cause an alarm to be displayed on a display component when    ventricular fibrillation/tachycardia is determined to occur.

By way of example, the four-dimensional feature space may comprisethreshold crossing sample count (TCSC), VF filter (VFleak), sampleentropy, and Count2 features. This list is not meant to be limiting, andother temporal, spectral, and complexity features are known in the art.See, e.g., Cheng and Dong, Digital Object Identifier10.1109/ACCESS.2017.2723258.

In certain embodiments, the first signal quality parameter is a kurtosisvalue calculated for each waveform in the plurality of waveforms. Theterm “kurtosis” refers to the a measure of the shape of a set of data,in this case of a frequency-distribution curve. More specifically,kurtosis measures the relative peakedness of a distribution with respectto a Gaussian distribution. In preferred embodiments, the kurtosis valuefor each waveform in the plurality of waveforms is calculated from atime window of a predetermined length in each waveform. By way ofexample only, the kurtosis value for each waveform may updated at aninterval of between 2 and 20 seconds, and preferably about every 3 toabout every 5 seconds. These intervals may be overlapping orconsecutive.

In certain embodiments, the second signal quality parameter isdetermined using a “cliff amplitude” (i.e., the signal amplitude at thepoint of detection) and an elapsed time since the previous valid QRScomplex identified. Methods for determining the second signal qualityparameter are described hereinafter.

The skilled artisan will understand that many approaches are availablefor QRS detection. First-derivative-based methods are often used inreal-time analysis or for large datasets since they do not requireextensive computations. These methods also have the advantage of notnecessitating manual segmentation of data, training of the algorithms,or patient-specific modifications that are often required for otherdetection methods. In certain embodiments, QRS complexes in the combinedECG waveform may be determined using a so-called Pan-Tompkins algorithmor a variation thereof. See, e.g., Pan and Tompkins, IEEE Trans. Eng.Biomed. Eng., 32: 230-36, 1985; Hamilton and Tompkins, IEEE Trans. Eng.Biomed. Eng. 12: 1157-1165, 1986; Arzeno et al., IEEE Trans. Eng.Biomed. Eng. 55: 478-84, 2008.

In a related aspect, the invention relates to methods for continuouslymonitoring a patient for cardiac electrical abnormalities, comprising:

-   obtaining a plurality of time-dependent electrocardiogram (ECG)    waveforms from an ECG sensor comprising plurality of ECG electrodes,    each waveform in the plurality of waveforms corresponding to    electrical signals obtained from one ECG electrode in the plurality    of ECG electrodes;-   processing the plurality of waveforms by    -   determining a time-dependent first signal quality parameter for        each waveform in the plurality of waveforms and curating the        plurality of waveforms by comparing each first signal quality        parameter to a first quality threshold metric,    -   wherein if at least one first signal quality parameter exceeds        the first quality threshold metric, accepting those waveforms        having a first signal quality parameter that exceed the first        quality threshold metric and discarding those waveforms having a        first signal quality parameter that does not exceed the first        quality threshold metric, or if no first signal quality        parameter exceeds the first quality threshold metric, accepting        all waveforms, and

combining the accepted waveforms to provide a time-dependent combinedECG waveform.

In a further related aspect, the present invention provides systemsadapted for continuously monitoring a patient for cardiac electricalabnormalities according to the foregoing methods. Such systems comprise:

an ECG sensor comprising plurality of ECG electrodes configured to beworn on the patient's body, the sensor configured to generate aplurality of time-dependent ECG waveforms, each waveform in theplurality of waveforms corresponding to electrical signals obtained fromone ECG electrode in the plurality of ECG electrodes;

a processing component configured to receive and process the pluralityof time-dependent ECG waveforms by

-   -   determining a time-dependent first signal quality parameter for        each waveform in the plurality of waveforms and curating the        plurality of waveforms by comparing each first signal quality        parameter to a first quality threshold metric,    -   wherein if at least one first signal quality parameter exceeds        the first quality threshold metric, accepting those waveforms        having a first signal quality parameter that exceed the first        quality threshold metric and discarding those waveforms having a        first signal quality parameter that does not exceed the first        quality threshold metric, or if no first signal quality        parameter exceeds the first quality threshold metric, accepting        all waveforms, and    -   combining the accepted waveforms to provide a time-dependent        combined ECG waveform;

-   the processing component further configured to process the combined    ECG waveform to by    -   identifying each QRS complex in the combined ECG waveform,    -   determining a second signal quality parameter for each QRS        complex by gravity cliff detection,    -   curating each second signal quality parameter by comparing each        second signal quality parameter to a second quality threshold        metric, wherein if the second signal quality parameter exceeds        the second quality threshold metric, the QRS complex is        identified as a valid QRS complex;    -   determining the occurrence or nonoccurrence of asystole and/or        atrial fibrillation from the valid QRS complexes; and    -   cause an alarm to be displayed on a display component when        asystole and/or atrial fibrillation is determined to occur.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 depicts an exemplary body worn vital signs monitoring system.

FIG. 2 depicts a block diagram of the asystole/atrial fibrillationmonitoring system of the present invention.

FIG. 3 depicts an exemplary plot of ECG lead signals and theircorresponding kurtosis values.

FIG. 4 depicts a flow diagram for an exemplary Lead Select algorithm.

FIG. 5 depicts a raw ECG waveform and a corresponding Pan-Tompkinswaveform which demonstrates a GCD picker being applied in graphicalform.

FIG. 6 depicts RR interval variability in an ECG signal and acorresponding cosine similarity analysis of the ECG signal in a patientwith atrial fibrillation.

FIG. 7 depicts RR interval variability in an ECG signal and acorresponding cosine similarity analysis of the ECG signal in a normalsubject.

FIG. 8 depicts an analysis as described for FIGS. 6 and 7, but appliedto false beats due to signal artifacts.

FIG. 9 depicts a flow diagram for an exemplary RR interval screeningalgorithm.

FIG. 10 depicts an exemplary SAMPEN and RMSSD before and after the onsetof AFIB.

FIG. 11 depicts an exemplary time series of ECG trace, Count 2, TCSC,VFleak, and Sample entropy before and after a VFIB/VTACH event.

FIG. 12 depicts an exemplary adaptive boosting tree (AdaBoost) machinelearning classifier for application to identify a VFIB/VTACH patientevent.

FIG. 13 depicts an exemplary time series of probabilities generated bythe AdaBoost classifier before and after a VFIB/VTACH event.

FIG. 14 depicts an exemplary time series of ECG, PPG, TCSC, and sampleentropy being applied to distinguish normal sinus rhythm from otherrhythms and artifact in the PPG.

FIG. 15 depicts a flow diagram for an exemplary ventricular fibrillationevent screening algorithm.

FIG. 16 depicts an exemplary time series of ECG, body wornaccelerometer, and VFIB/VTACH features to illustrate their sensitivityto motion artifact caused by patient activity as measured by theaccelerometer signals.

FIG. 17 depicts a flow diagram for an exemplary algorithm to correlatebetween activity and the predictive features indicative of false alarms,VFIB/VTACH and AFIB.

FIG. 18 depicts a flow diagram for fusion of PPG and ACC waveforms withECG waveforms to identify LTA+AF.

DETAILED DESCRIPTION OF THE INVENTION

System Overview

For purposes of the present application, the following abbreviationsapply:

Terminology Definition ECG Electrocardiogram ASYS Asystole AFIB or AFAtrial Fibrillation VFIB or VF Ventricular Fibrillation VTACHVentricular Tachycardia LTA + AF Life Threatening Arrhythmias plusAtrial Fibrillation RR Interval between successive QRS complexes HRHeart Rate PPV Positive Predictive Value PPG Photoplethysmogram PI PulseInterval ACC Accelerometer PWD Patient Worn Device RVD Remote ViewingDevice RMSSD Root mean square of successive differences SAMPEN SampleEntropy

For purposes of example only, the present invention is described interms of using the ViSi Mobile® vital sign monitoring system (SoteraWireless, Inc.). The ViSi Mobile system is a body-worn vital signmonitor that continuously measures heart rate, SpO2, respiration rate,pulse rate, blood pressure, and skin temperature. The body worn monitoris comprised of a wrist device and a cable, which includes an upper armmodule and a chest module as shown in FIG. 1. The wrist device, upperarm module, and chest module each contain a three-axis accelerometer. Inaddition to the more traditional vital signs, the three accelerometersin the monitor capture data that can be used to estimate a patient'sposture, the time spent in a specific posture, detect when a patient hasfallen, and determine when the patient is walking.

The algorithm used to classify the life-threatening arrhythmias andatrial fibrillation can be provided on integrated circuitry within thechest module to measure and digitize ECG signals. The embedded softwareused to implement the algorithm is executed on a microprocessor locatedin the chest module.

A block diagram of the asystole/atrial fibrillation monitoring system ofthe present invention is shown in FIG. 2. The following describes thissystem in more detail.

ECG Filter & Lead Select

Filter

ECG waveforms for a three-wire cable (leads I, II, & III) and afive-wire cable (leads I, II, III, & V) are transduced and digitizedusing a Texas Instruments ADS1298R. The waveforms are digitized using a24-bit delta-sigma analog to digital converter. The gain setting on theamplifier of the ADS1298R is six. The lowest significant bit in thedigitized waveform is equivalent to 0.04768 microvolts. The ECGbeat-picker and LTA+AF algorithms have some pre-defined thresholds thatare sensitive to the scale of the waveform and any changes to thedefinition of the LSB would need to be propagated to these thresholds.

All available leads are sampled at a rate of 500 Hz and a digital filteris applied to them prior to their use by the ECG beat-picker and LTA+AFalgorithms. The digital filter is a comb filter that provides a highpass −3 dB cut-off frequency of 0.5 Hz and has notch filters atmultiples of 60 Hz.

Lead Select

ECG signal noise due to lead movement or muscle artifact may corrupt asingle ECG lead or multiple ECG leads simultaneously. A challenge for amulti-lead ECG system is to develop an algorithm to combine or arbitratebetween information from the different leads to improve the accuracy ofany vital sign or event classification derived from these signals.Although kurtosis has been described in literature as a useful metric todistinguish between ECG signals with and without noise, the presentinvention utilizes a unique implementation in which this statisticalmetric is used to combine or exclude ECG leads from the ECG QRSdetection algorithm used for heart rate calculation and AFclassification.

A flow chart for the Lead Select algorithm is depicted in FIG. 4. TheECG beat-picker and atrial fibrillation algorithm utilize leads I, II,and III or any combination of those leads. An algorithm was developed toclassify the quality of these three leads and automatically select asingle lead or combination of the leads based on their signal quality.

The signal quality of an ECG lead is determined using a statisticalmeasure of the signal known as kurtosis. The kurtosis of each ECG leadis calculated from a windowed ECG signal 8.192 seconds in length orusing 4096 samples. The kurtosis of each lead is updated every 4seconds. The equation given in (1) shows the kurtosis calculation forECG lead k, where N=4096 ECG samples y_(k), in the buffer with a meansignal value, y _(k). This calculation is performed for leads, k=I, II,III.

$\begin{matrix}{{{kurtosis}_{k}\lbrack i\rbrack} = \frac{\frac{1}{N}{\sum\limits_{i - N + 1}^{i}\left( {{y_{k}\lbrack i\rbrack} - {\overset{\_}{y}}_{k}} \right)^{4}}}{\left( {\frac{1}{N}{\sum\limits_{i - N + 1}^{i}\left( {{y_{k}\lbrack i\rbrack} - {\overset{\_}{y}}_{k}} \right)^{2}}} \right)^{2}}} & (1)\end{matrix}$

A fixed threshold is used to evaluate the quality of each lead using thecalculated kurtosis. If the kurtosis is above the threshold that lead isprocessed through a filter chain and fused with any other leads that arealso above threshold. The fused waveform is then used by the beat-pickerand feature extraction algorithms to classify atrial fibrillation. Ifall of the leads fall below the fixed threshold (e.g. 5) then all of theleads are accepted, processed through the filter chain, and fused foruse by the ECG beat-picker and for feature extraction. A sample plot ofECG leads and their corresponding kurtosis values are shown in FIG. 3.

Similarly, the ventricular tachycardia and ventricular fibrillationalgorithm may also utilize the kurtosis metric to select the appropriateleads for feature extraction and classification. The VTACH/VFIBalgorithm extracts features from two ECG leads for classification. Theleads are evaluated in order of preference V, II, I, and III dependingon their availability. If the kurtosis of the lead is above thethreshold it is used to generate features for the classifier. If thekurtosis of all of the leads are below the threshold then the two leadsare selected in the order of preference depending on their availability.

ECG Beat Detector

Fiducial point selection of the QRS complex is critical for thetime-dependent measurement of cNIBP. Electrode preparation, placement,electrical conduction, and cardiac axis all affect the QRS complexmorphology. A change in any of these would cause errors in the temporalmeasurement of the QRS.

Pan-Tompkins processing is used to detect the full width of the QRScomplex without distortion from changes in the Q, R or S waves. Out ofband noise is filtered out and does not distort the signal. Stablefiducial points on this peak are used for the cNIBP timing measurement.

The Gravity Cliff Detector is designed to reject in-band noise byselecting its parameters based on performance on challenging annotateddatasets. The look-behind style of beat detection permits all temporalinformation to be available at the time of the detection decision, whichremoves the need to carefully manage the internal states of thedetector.

Pan-Tompkins (PT) Signal Processing

A 5 to 15 Hz bandpass filter is applied to each signal, to selectfrequencies common to the QRS complex.

-   -   6-pole Butterworth band-pass filter    -   Cascaded set of three different 2-pole filters    -   Designed in floating point, scaled to use integer coefficients    -   29 sample delay

A five-point derivative filter is applied to each signal, to accentuaterapid changes in voltage, common in the QR and RS segments.y(t)= 1/64[x(t)+16·x(t−4)−16·x(t−12)−x(t−16)]

-   -   8 sample delay

A squaring stage is applied to each signal, to magnify large values andrectify negative values.y(t)=[x(t)]²

-   -   No delay

A moving window sums each signal, creating peaks where QRS complexesexist amongst a low noise floor.y(t)=Σ_(n=0) ⁷⁵ x(t−n)

-   -   38 sample delay

Valid leads are on the patient and have a kurtosis above a threshold.Processed signals from valid leads are averaged to create a singlesignal. This signal is always positive, and QRS complexes appear aspeaks.

Gravity Cliff Detection (GCD)

The beat detection algorithm identifies beats on their falling edgerather than on their rising edge as is standard practice for traditionalbeat picking algorithms. This technique allows interrogation of theentire beat prior to its classification as a beat reducing the falsepositive rate.

The GCD picker is applied to the fused signal after being processedthrough the Pan-Tompkins filter chain. The GCD simulates constantnegative acceleration on a particle that is moving with time along thesignal. The magnitude of the signal is interpreted as a height value.When the particle drops below the signal height, the position is set tothe signal height and the velocity is set to zero, akin to hitting theground.

As the particle falls off the top of a peak in the signal, itaccelerates towards the signal baseline and its velocity increasesanalogous to a freefall. The particle position also moves closer to theamplitude of the current signal. While in this freefall state if thevelocity exceeds a threshold then a cliff is detected at the time andamplitude value of the signal at the start of the free fall period.Prior to being selected as a beat the candidate cliff point must meetseveral criteria outlined below.

-   -   cliff amplitude>minimum cliff height    -   duration since the last valid cliff>hold off duration

If the signal amplitude at the point of detection meets these criteriait is considered a valid beat and the particle is reset to the currentsignal height with a velocity of 0. The relevant variables andthresholds for the GCD are shown in FIG. 5.

Asystole

The Asystole determination relies on the ECG beat detector. If a normalor ventricular beat is not detected for a specified period of time themonitor will alarm on Asystole. The period of time is user configurablebetween 4-15 seconds.

The ViSi Mobile monitor can measure heart rate from both the ECG andpulse rate from the optical sensor at the base of the thumb. This allowsthe device to mitigate false Asystole calls on the ECG using pulse rate.The monitor will alarm on Asystole if a normal or ventricular beat isnot detected for a specified period of time and if there is not a valid,current pulse rate available. Pulse rate is determined as the medianpulse interval in a 15-second moving window. Pulse intervals arecalculated as the time difference between fiducial points on successivebeats detected in the photoplethysmogram (PPG) signal. The pulse ratealgorithm updates pulse rate every 3 seconds. If the number of PPG beatsin the 15-second window drops below a minimum of 3 beats, pulse ratewill not display a valid value and it will not suppress an Asystolealarm.

Atrial Fibrillation

The alarms for atrial fibrillation may be divided into twocategories: 1) atrial fibrillation with rapid ventricular response (AFIBRVR) and 2) atrial fibrillation with controlled ventricular response(AFIB CVR). The algorithm used to classify atrial fibrillation is thesame for both alarms they are differentiated only by the patient'scurrent heart rate.

The RR intervals measured by the ECG beat detector are the primary inputto the atrial fibrillation classifier. For every ECG beat detected an RRinterval is determined as the time difference between the fiducial pointmarking the current beat (t_(k)) and the fiducial point marking theprevious beat (t_(k−1)). The fiducial points for each ECG beat aredetermined as the midpoint of the integrated Pan-Tompkins waveform.

Cosine Similarity

Increased RR interval variability can be caused by atrial fibrillation,the presence of ventricular escape beats, and erroneous beat-picks dueto signal artifact. The morphology of adjacent beats can be compared andused to identify intervals that were derived between ventricular beats,normal beats, and signal artifact. Unwanted RR intervals can be excludedfrom the atrial fibrillation classifier to prevent false positive eventclassifications.

A flow diagram for RR interval screening is shown in FIG. 9. The methodused to compare adjacent beat morphology begins with the ECG beat-pickerthat identifies beats and determines a fiducial point on the signal thatis processed through the Pan-Tomkins filter chain.

The second stage of the method performs a comparison of the unfilteredECG signal before and after the fiducial points on the adjacent beats. Acosine similarity metric is used to compare ECG waveform segmentssurrounding the two fiducial points one which occurred at sample time jand the other at sample time k and then used to calculate the RRinterval (RR=k−j). The formula for cosine similarity is given in (2)where y[i] is the unfiltered ECG signal from a single lead and N=20 isthe number of samples included before and after the fiducial point.

$\begin{matrix}{{similarity} = \frac{\sum\limits_{i = {- N}}^{N}{{y\left\lbrack {j + i} \right\rbrack}{y\left\lbrack {k + i} \right\rbrack}}}{\sqrt{\sum\limits_{i = {- N}}^{N}\left( {y\left\lbrack {j + i} \right\rbrack} \right)^{2}}\sqrt{\sum\limits_{i = {- N}}^{N}\left( {y\left\lbrack {k + i} \right\rbrack} \right)^{2}}}} & (2)\end{matrix}$

FIG. 6 shows a cosine similarity analysis of an ECG lead trace for apatient with atrial fibrillation; FIG. 7 shows a similar analysis for apatient exhibiting normal and ventricular beats; and FIG. 8 shows asimilar analysis of false beats due to signal artifacts. If the cosinesimilarity metric is above a pre-specified threshold (e.g. 0.745) theAFIB classifier uses the RR interval, if it is below the threshold it isexcluded from being used to calculate the two features. The AFIBclassifier is updated every 30 seconds. The classifier uses a 136 secondmoving average window. The 90 most recent RR intervals are used togenerate the features in the classifier. If 60 acceptable RR intervalsare not contained in the 136-second window the algorithm will not updatethe AFIB classification.

SAMPEN & RMSSD

The classification method uses two features to classify AFIB. The firstfeature RMSSD is the root mean square of successive differences in RRintervals. The second feature SAMPEN is the sample entropy of thesuccessive differences in RR intervals. FIG. 10 shows an exemplarySAMPEN and RMSSD before and after the onset of AFIB (onset indicated bythe dashed line).

ECG AFIB Classifier

The machine learning classifier used to determine AFIB in thistwo-dimensional feature space is based on a geometric approximation ofthe classification regions described by a support vector machineclassifier trained on annotated ECG data. The support vector machineutilized radial basis function kernels and was not computationallyefficient enough to be implemented into the embedded software.Therefore, multiple circles and arcs were used to approximate the AFIBclassification region and allow a simplified embedded implementation. Ifthe features described a point located within the classification regionthe model classified the features as AFIB. If they describe a pointoutside of the classification region the model classified the featuresas Not AFIB.

FIG. 11 shows an exemplary time series of ECG trace, Count 2, TCSC,VFleak, and Sample entropy before and after a VFIB/VTACH event. An AFIBalert is triggered when AFIB is classified for two consecutive updates.The type of AFIB message that is displayed depends on the patient'sheart rate. If the Heart rate is above the user-defined value, then thedevice alerts with the message “AFIB-RVR” for rapid ventricularresponse. If the patient's heart rate is below the user-defined valuethe device alerts with the message “AFIB-CVR” for controlled ventricularresponse.

The heart rate algorithm utilizes a 20 second moving window to determineheart rate. The heart rate algorithm updates heart rate at 1-secondintervals. For every ECG beat detected an RR interval is determined asthe time difference between the fiducial point marking the current beatand the fiducial point marking the previous beat. The heart rate isdetermined as the inverse of the average RR intervals of all of the ECGbeats detected in the 20-second window. If an ECG beat is not detectedin the last 3 seconds prior to the time of the current HR update anadditional RR interval is added to the sum used to determine the averageinterval. The additional RR interval is calculated as the timedifference between the update time and the last ECG beat detected. If noECG beats are detected in the 20-second interval the heart rate is setequal to 0.

Ventricular Tachycardia and Ventricular Fibrillation

A set of 4 features derived from the ECG waveform is used to classifyrapid ventricular tachycardia (VTACH) and ventricular fibrillation(VFIB). The classifier does not distinguish between the two differentlife-threatening arrhythmias and generates a single alarm to alert inthe event that either of them is detected (“VTACH/VFIB”).

The inputs to the VFIB/VTACH algorithm are non-overlapping windowedsegments of the filtered ECG waveform. The windows are 8.192 seconds induration. Prior to windowing the data, the ECG waveform was down-sampledfrom 500 Hz to 125 Hz in order to maximize efficiency and minimize thememory required to generate the features. Each windowed data segmentconsists of 1024 ECG samples.

Count2, TCSC, VFleak, and Sample Entropy

The method generates four features from each windowed ECG data segment.These features are used as inputs into the machine learning classifierused to detect VFIB/VTACH. The four features are Count 2, TCSC, VFleak,and sample entropy.

The method also finds the minimum and maximum value of the signal overeach window. At the end of each window the difference between themaximum and minimum values are taken. If the difference is less then 150then a “flat-line” condition is flagged, and the lead will not be usedfor classification.

ECG/VFIB/VTACH Classifier

The method utilizes a machine learning classifier to determineVFIB/VTACH in a complex four-dimensional feature space that can beimplemented on the constrained resources of a small, body worn,low-power, embedded system.

An adaptive boosting tree (AdaBoost) machine learning classifier wasdeveloped to determine if the four features indicate a VFIB/VTACHpatient event. The classifier employs 163 decision trees each having amaximum depth of two decision nodes. Each leaf of the tree provides alogarithmic probability for both of the possible classifications (0or 1) as shown in FIG. 12, where the circles indicate the decision nodeswhich can operate on any one of the 4 features and the ovals are theunique logarithmic probabilities for each leaf.

The output of the classifier LogProbSum for a single ECG lead is theweighted sum of the log probabilities from each decision tree i, forboth classes as shown in (3a) and (3b). The output of each tree ismultiplied by a unique weight, w_(i).LogProbSum₀=Σ_(i=0) ¹⁶² w _(i)×LogProb₀[i]  (3a)LogProbSum₁=Σ_(i=0) ¹⁶² w _(i)×LogProb₁[i]  (3b)

The probability P₁ that a VFIB/VTACH event has occurred is determinedusing the equation in (4)

$\begin{matrix}{P_{1} = \frac{\exp\left( {{Log}\;{ProbSum}_{1}} \right)}{{\exp\left( {{Log}\;{ProbSum}_{0}} \right)} + {\exp\left( {{Log}\;{ProbSum}_{1}} \right)}}} & (4)\end{matrix}$

Four features are simultaneously calculated on two independent ECGleads. The features generated from each lead allow the AdaBoostclassifier to generate a unique probability for each lead every8.192-second window. The two leads used by the algorithm to generate thefeatures depend on the available leads for a three or five wire cable.The priority of the two leads used by the algorithm is provided below inranked order: (1) Lead V, (2) Lead II, (3) Lead I, and (4) Lead III.This priority was determined based on an analysis of sensitivity andspecificity on annotated patient data.

An exemplary time series of probabilities generated by the AdaBoostclassifier before and after a VFIB/VTACH event is shown in FIG. 13. TheVFIB/VTACH alarm is triggered when the probability of VFIB/VTACHdetermined by the AdaBoost classifier for both leads surpass apre-specified threshold for two consecutive windows. VFIB/VTACH alarmwill be cancelled when both leads fall below a different pre-specifiedthreshold for two consecutive windows.

Arrythmia Reconciliation

The LTA+AF classifiers for ECG are independent of each other and on rareoccasions require some arbitration in terms of alert priority.Additionally, in some instances the arrhythmia classifications dictatewhether heart rate or pulse rate are displayed on the PWD or RVD. Thearrhythmia alerts are prioritized in the following order: (1)VFIB/VTACH, (2) Asystole, (3) AFIB-RVR/AFIB-CVR.

ECG Noise and Artifact Classifier

The AdaBoost VFIB/VTACH can be extended to include a classifier fornoise or artifact. Using the same underlying feature set, the classifiercan be trained to output probabilities for three possibleclassifications (VFIB/VTACH, Noise/Artifact, Other).

The Noise/Artifact probability can serve two roles within the system.First, to suppress heart rate calculation during periods of excessiveartifact or noise; second, to provide additional information to thereconciliation step which can be fused with other modalities (asdiscussed below) to help distinguish between VFIB/VTACH and artifactthat closely resembles VFIB/VTACH.

PPG/ECG Fusion

As a measure of pulsatile activity, the photoplethysmogram (PPG)provides another window into a patient's cardiac rhythms. Arrhythmiassuch as VTACH, VFIB, and AFIB not only alter the timing between pulsesbut also alter end diastolic volume leading to large variations inpre-ejection period and left ventricular ejection time, stroke volume,and pulse amplitude all detectable using the PPG and Pulse Arrival Time.

PPG/AFIB Classifier

A significant challenge for an AFIB classifier based on RR intervalvariability are the false positives generated when the ECG signal iscorrupt by artifact. If the source of the artifact in the ECG signals isindependent from artifact in the PPG signals, the PPG signal may providea methodology to suppress false AFIB classifications. Additionally, whenonly the PPG signals are available the signals may be used to classifyAFIB independently.

With some minor modification, the feature extraction methods that wereapplied to RR intervals in the ECG (SAMPEN & RMSSD) can also be appliedto pulse intervals (PI) measured by the PPG for classification of AtrialFibrillation. A region can be defined in the two-dimensional featurespace derived from the pulse intervals to delineate AFIB from NON-AFIBusing a variety of machine learning techniques such as an ensemblemethod like the AdaBoost algorithm or support vector machine using avariety of kernels to map the features to higher dimensions. If the ECGsignals contain artifact that cause a false AFIB classification based onRR intervals and the PI intervals derived from PPG indicate that thepatient is not in AFIB using a separate classifier the alarm could besuppressed or it could be delayed for a period of time.

Alternatively, the two PI based features SAMPEN_(PI) & RMSSD_(PI)derived from the PPG signals could be combined with the SAMPEN_(RR) andRMSSD_(RR) features derived from the RR intervals measured with the ECGsignals to create a four-dimensional feature space that can bedelineated into two regions AFIB and NON-AFIB and used forclassification and to generate an AFIB alarm

PPG/VFIB/VTACH Classifier

A significant challenge for VFIB classification are the false positivesgenerated when the ECG signal is corrupt by artifact. If the source ofthe artifact in the ECG signals is independent from artifact in the PPGsignals, the PPG signal may provide a methodology to suppress false VFIBclassifications. FIG. 14 shows an exemplary time series of ECG, PPG,TCSC, and sample entropy illustrate how these features can be used todistinguish normal sinus rhythm from other rhythms and artifact in thePPG. The dashed red line on ECG indicate the onset of a VFIB/VTACHevent.

With some minor modification, two of the four feature extractionalgorithms that were applied to the ECG signals (TCSC & SAMPEN) can alsobe applied to PPG signals for verification of a Ventricular Fibrillationevent. FIG. 15 shows a flow diagram of such an implementation. A regioncan be defined in the two-dimensional feature space derived from the PPGsignals to delineate sinus rhythm pulses from VFIB and other types ofsignal artifact using a variety of machine learning techniques such asan ensemble method like the AdaBoost algorithm or support vector machineusing a variety of kernels to map the features to higher dimensions. Ifthe ECG signals contain artifact that cause a false VFIB/VTACHclassification and the PPG signal provides features (TCSC & SAMPEN) intoa separate classifier to label the patient in normal sinus rhythm theannunciation of the alarm could be suppressed, be delayed for a periodof time, or postponed for a series of consecutive VFIB/VTACHclassifications from the ECG signals.

Accelerometer/ECG Fusion

As a measure of patient activity, the three accelerometers integratedinto the ViSi Mobile monitor provide additional context to the VFIB,VTACH, and Asystole classifications. Knowledge of the type and level ofpatient activity can be used to extend the requirements on the number ofconsecutive classifications required to trigger an alarm or eliminatesmall time windows of data from being processed by the featureextraction algorithms. For example, an algorithm can be used todetermine if significant changes in the features used to classifyVFIB/VTACH and AFIB are correlated to changes in the patient's activitytype or activity level.

FIG. 16 shows an exemplary Time series of ECG, body worn accelerometer,and VFIB/VTACH features illustrate their sensitivity to motion artifactcaused by patient activity which is measured by the accelerometersignals. If VFIB/VTACH and AFIB signals are correlated to activity, thenumber of consecutive classifications required to alarm could beincreased during these activity periods. Additionally, in the case oflife threatening arrhythmias such as VFIB and Asystole, the ViSi Mobilesystem can self-annotate patient activities that cause false alarmsbased on resumption of a non-lethal rhythm following the alarm. Thisallows adaptive learning about the correlation between activity and thepredictive features. A flow diagram of such a system is shown in FIG.17. Similarly, Both the PPG and ACC can be fused with the ECG into theLTA+AF methods described herein as shown in FIG. 18.

The following are preferred embodiments of the invention.

-   Embodiment 1. A method for continuously monitoring a patient for    cardiac electrical abnormalities, comprising:-   obtaining a plurality of time-dependent electrocardiogram (ECG)    waveforms from an ECG sensor comprising plurality of ECG electrodes,    each waveform in the plurality of waveforms corresponding to    electrical signals obtained from one ECG electrode in the plurality    of ECG electrodes;-   processing the plurality of waveforms by    -   determining a time-dependent first signal quality parameter for        each waveform in the plurality of waveforms and curating the        plurality of waveforms by comparing each first signal quality        parameter to a first quality threshold metric,    -   wherein if at least one first signal quality parameter exceeds        the first quality threshold metric, accepting those waveforms        having a first signal quality parameter that exceed the first        quality threshold metric and discarding those waveforms having a        first signal quality parameter that does not exceed the first        quality threshold metric, or if no first signal quality        parameter exceeds the first quality threshold metric, accepting        all waveforms, and

combining the accepted waveforms to provide a time-dependent combinedECG waveform;

-   processing the combined ECG waveform to by    -   identifying each QRS complex in the combined ECG waveform,    -   determining a second signal quality parameter for each QRS        complex by gravity cliff detection, and    -   curating each second signal quality parameter by comparing each        second signal quality parameter to a second quality threshold        metric, wherein if the second signal quality parameter exceeds        the second quality threshold metric, the QRS complex is        identified as a valid QRS complex;-   determining the occurrence or nonoccurrence of asystole and/or    atrial fibrillation from the valid QRS complexes; and-   causing an alarm to be displayed on a display component when    asystole and/or atrial fibrillation is determined to occur.-   Embodiment 2. A method according to embodiment 1, wherein the method    comprises determining the occurrence or nonoccurrence of asystole,    wherein asystole is determined to occur when no valid QRS complexes    are identified over a predetermined time period.-   Embodiment 3. A method according to embodiment 1 or 2, wherein the    method comprises determining the occurrence or nonoccurrence of    atrial fibrillation, wherein atrial fibrillation is determined by,    for a plurality of pairs of consecutive valid QRS complexes    occurring over a predetermined time period,    -   for each consecutive pair of valid QRS complexes, determining an        interval between a first fiducial point in the first member of        the consecutive pair to a corresponding fiducial point in the        first member of the consecutive pair, thereby providing a        plurality of intervals,    -   curating the plurality of intervals by calculating a third        signal quality parameter for each interval and comparing each        third signal quality parameter to a third quality threshold        metric, wherein if the third signal quality parameter exceeds        the third quality threshold metric, the interval is identified        as a valid interval, and    -   classifying whether the valid intervals obtained from the        plurality of pairs of consecutive valid QRS complexes are        indicative of atrial fibrillation.-   Embodiment 4. A method according to embodiment 3, wherein the    classifying step comprises calculating a root mean square of    successive differences in the valid intervals.-   Embodiment 5. A method according to embodiment 3 or 4, wherein the    classifying step comprises calculating a sample entropy of    successive differences in the valid intervals.-   Embodiment 6. A method according to embodiment 3, wherein the    classifying step comprises calculating a two dimensional space that    is a function of a root mean square of successive differences in the    valid intervals and a sample entropy of successive differences in    the valid intervals, and defining values that fall within an area    within the two dimensional space as being indicative of the    occurrence of atrial fibrillation.-   Embodiment 7. A method according to one of embodiments 1-6, wherein    the method further comprises determining the occurrence or    nonoccurrence of ventricular fibrillation/tachycardia by-   processing at least two of the plurality of time-dependent ECG    waveforms by    -   selecting from each of the at least two ECG waveforms, a first        waveform segment of time length t, and a second waveform segment        of time length t, wherein the first and second waveform segments        are non-overlapping consecutive segments, and    -   for each of the first and second waveform segments, calculating        a four-dimensional feature space comprising at least one        temporal feature, at least one spectral feature, and at least        one a complexity feature;-   for each of the at least two ECG waveforms, determining if the    four-dimensional feature space is indicative of the occurrence of    ventricular fibrillation/tachycardia, wherein if ventricular    fibrillation/tachycardia is indicated by processing of each of the    at least two ECG waveforms, the occurrence ventricular    fibrillation/tachycardia is determined; and-   cause an alarm to be displayed on a display component when    ventricular fibrillation/tachycardia is determined to occur.-   Embodiment 8. A method according to embodiment 7, wherein the    four-dimensional feature space comprises threshold crossing sample    count (TCSC), VF filter (VFleak), sample entropy, and Count2    features.-   Embodiment 9. A method according to one of embodiments 1-8, wherein    the first signal quality parameter is a kurtosis value calculated    for each waveform in the plurality of waveforms.-   Embodiment 10. A method according to embodiment 9, wherein the    kurtosis value for each waveform in the plurality of waveforms is    calculated from a time window of a predetermined length in each    waveform.-   Embodiment 11. The method of embodiment 9 or 10, wherein the    kurtosis value for each waveform is updated at an interval of    between 2 and 20 seconds, and preferably about every 3 to about    every 5 seconds.-   Embodiment 12. A method according to one of embodiments 1-11,    wherein the second signal quality parameter is determined using a    cliff amplitude and an elapsed time since the previous valid QRS    complex identified.-   Embodiment 13. A method according to one of embodiments 1-12,    wherein each QRS complex in the combined ECG waveform is determined    using a Pan-Tompkins algorithm.-   Embodiment 14. A method according to one of embodiments 1-13,    further comprising-   determining an activity type or activity level for the patient using    time-dependent motion waveforms obtained from one or more    accelerometers worn on the patient's body, and if the occurrence of    asystole and/or atrial fibrillation is determined,    -   determining if the activity type or activity level is        inconsistent with the occurrence of asystole and/or atrial        fibrillation and, if so, causing the alarm to be displayed on a        display component to be modified.-   Embodiment 15. A method according to embodiment 14, wherein the    alarm is modified by being suppressed during the period of    inconsistency.-   Embodiment 16. A method according to embodiment 14, wherein the    alarm is modified by requiring multiple consecutive asystole and/or    atrial fibrillation determinations be identified in order for the    alarm to be displayed on the display component.-   Embodiment 17. A method according to one of embodiments 1-16,    further comprising-   determining a photoplethysmogram for the patient using    time-dependent optical waveforms obtained from an optical sensor    worn on the patient's body, and if the occurrence of asystole and/or    atrial fibrillation is determined,    -   determining if the photoplethysmogram is inconsistent with the        occurrence of asystole and/or atrial fibrillation and, if so,        causing the alarm to be displayed on a display component to be        modified.-   Embodiment 18. A method according to embodiment 17, wherein the    alarm is modified by being suppressed during the period of    inconsistency.-   Embodiment 19. A method according to embodiment 17, wherein the    alarm is modified by requiring multiple consecutive asystole and/or    atrial fibrillation determinations be identified in order for the    alarm to be displayed on the display component.-   Embodiment 20. A method according to one of embodiments 7-19,    further comprising-   determining an activity type or activity level for the patient using    time-dependent motion waveforms obtained from one or more    accelerometers worn on the patient's body, and if the occurrence of    ventricular fibrillation/tachycardia is determined,    -   determining if the activity type or activity level is        inconsistent with the occurrence of ventricular        fibrillation/tachycardia and, if so, causing the alarm to be        displayed on a display component to be modified.-   Embodiment 21. A method according to embodiment 20, wherein the    alarm is modified by being suppressed during the period of    inconsistency.-   Embodiment 22. A method according to embodiment 20, wherein the    alarm is modified by requiring multiple consecutive asystole and/or    atrial fibrillation determinations be identified in order for the    alarm to be displayed on the display component.-   Embodiment 23. A method according to one of embodiments 7-22,    further comprising-   determining a photoplethysmogram for the patient using    time-dependent optical waveforms obtained from an optical sensor    worn on the patient's body, and if the occurrence of ventricular    fibrillation/tachycardia is determined,    -   determining if the photoplethysmogram is inconsistent with the        occurrence of ventricular fibrillation/tachycardia and, if so,        causing the alarm to be displayed on a display component to be        modified.-   Embodiment 24. A method according to embodiment 23, wherein the    alarm is modified by being suppressed during the period of    inconsistency.-   Embodiment 25. A method according to embodiment 23, wherein the    alarm is modified by requiring multiple consecutive asystole and/or    atrial fibrillation determinations be identified in order for the    alarm to be displayed on the display component.-   Embodiment 26. A system for continuously monitoring a patient for    cardiac electrical abnormalities, comprising:

an ECG sensor comprising plurality of ECG electrodes configured to beworn on the patient's body, the sensor configured to generate aplurality of time-dependent ECG waveforms, each waveform in theplurality of waveforms corresponding to electrical signals obtained fromone ECG electrode in the plurality of ECG electrodes;

a processing component configured to receive and process the pluralityof time-dependent ECG waveforms by

-   -   determining a time-dependent first signal quality parameter for        each waveform in the plurality of waveforms and curating the        plurality of waveforms by comparing each first signal quality        parameter to a first quality threshold metric,    -   wherein if at least one first signal quality parameter exceeds        the first quality threshold metric, accepting those waveforms        having a first signal quality parameter that exceed the first        quality threshold metric and discarding those waveforms having a        first signal quality parameter that does not exceed the first        quality threshold metric, or if no first signal quality        parameter exceeds the first quality threshold metric, accepting        all waveforms, and    -   combining the accepted waveforms to provide a time-dependent        combined ECG waveform;

-   the processing component further configured to process the combined    ECG waveform to by    -   identifying each QRS complex in the combined ECG waveform,    -   determining a second signal quality parameter for each QRS        complex by gravity cliff detection,    -   curating each second signal quality parameter by comparing each        second signal quality parameter to a second quality threshold        metric, wherein if the second signal quality parameter exceeds        the second quality threshold metric, the QRS complex is        identified as a valid QRS complex;    -   determining the occurrence or nonoccurrence of asystole and/or        atrial fibrillation from the valid QRS complexes; and    -   cause an alarm to be displayed on a display component when        asystole and/or atrial fibrillation is determined to occur.

-   Embodiment 27. A system according to embodiment 26, wherein the    processing component is configured to determine the occurrence or    nonoccurrence of asystole, wherein asystole is determined to occur    when no valid QRS complexes are identified over a predetermined time    period.

-   Embodiment 28. A system according to embodiment 26 or 27, wherein    the processing component is configured to determine the occurrence    or nonoccurrence of atrial fibrillation, wherein the processing    component determines atrial fibrillation by, for a plurality of    pairs of consecutive valid QRS complexes occurring over a    predetermined time period,    -   for each consecutive pair of valid QRS complexes, determining an        interval between a first fiducial point in the first member of        the consecutive pair to a corresponding fiducial point in the        first member of the consecutive pair, thereby providing a        plurality of intervals,    -   curating the plurality of intervals by calculating a third        signal quality parameter for each interval and comparing each        third signal quality parameter to a third quality threshold        metric, wherein if the third signal quality parameter exceeds        the third quality threshold metric, the interval is identified        as a valid interval, and    -   classifying whether the valid intervals obtained from the        plurality of pairs of consecutive valid QRS complexes are        indicative of atrial fibrillation.

-   Embodiment 29. A system according to embodiment 28, wherein the    classifying step comprises using the processing component to    calculate a root mean square of successive differences in the valid    intervals.

-   Embodiment 30. A system according to embodiment 28 or 29, wherein    the classifying step comprises using the processing component to    calculate a sample entropy of successive differences in the valid    intervals.

-   Embodiment 31. A system according to embodiment 28, wherein the    classifying step comprises using the processing component to    calculate a two dimensional space that is a function of a root mean    square of successive differences in the valid intervals and a sample    entropy of successive differences in the valid intervals, and to    define values that fall within an area within the two dimensional    space as being indicative of the occurrence of atrial fibrillation.

-   Embodiment 32. A system according to one of embodiments 26-31,    wherein the processing component is further configured to determine    the occurrence or nonoccurrence of ventricular    fibrillation/tachycardia by

-   processing at least two of the plurality of time-dependent ECG    waveforms by    -   selecting from each of the at least two ECG waveforms, a first        waveform segment of time length t, and a second waveform segment        of time length t, wherein the first and second waveform segments        are non-overlapping consecutive segments, and    -   for each of the first and second waveform segments, calculating        a four-dimensional feature space comprising at least one        temporal feature, at least one spectral feature, and at least        one a complexity feature;    -   for each of the at least two ECG waveforms, determining if the        four-dimensional feature space is indicative of the occurrence        of ventricular fibrillation/tachycardia, wherein if ventricular        fibrillation/tachycardia is indicated by processing of each of        the at least two ECG waveforms, the occurrence ventricular        fibrillation/tachycardia is determined; and    -   causing an alarm to be displayed on a display component when        ventricular fibrillation/tachycardia is determined is determined        to occur.

-   Embodiment 33. A system according to embodiment 32, wherein the    four-dimensional feature space comprises threshold crossing sample    count (TCSC), VF filter (VFleak), sample entropy, and Count2    features.

-   Embodiment 34. A system according to one of embodiments 26-33,    wherein the first signal quality parameter is a kurtosis value    calculated for each waveform in the plurality of waveforms.

-   Embodiment 35. A system according to embodiment 34, wherein the    kurtosis value for each waveform in the plurality of waveforms is    calculated from a time window of a predetermined length in each    waveform.

-   Embodiment 36. The system of embodiment 34 or 36, wherein the    kurtosis value for each waveform is updated at an interval of    between 2 and 20 seconds, and preferably about every 3 to about    every 5 seconds.

-   Embodiment 37. A system according to one of embodiments 26-36,    wherein the second signal quality parameter is determined using a    cliff amplitude and an elapsed time since the previous valid QRS    complex identified.

-   Embodiment 38. A system according to one of embodiments 26-37,    wherein each QRS complex in the combined ECG waveform is determined    using a Pan-Tompkins algorithm.

-   Embodiment 39. A system according to one of embodiments 26-38,    further comprising one or more accelerometers configured to be worn    on the patient's body and generate one or more time-dependent motion    waveforms indicative of patient motion, wherein the processing    component if configured to receive and process the one or more    time-dependent motion waveforms to determine an activity type or    activity level for the patient,

-   wherein if the occurrence of asystole and/or atrial fibrillation is    determined, the processing component is further configured to    determine if the activity type or activity level is inconsistent    with the occurrence of asystole and/or atrial fibrillation and, if    so, cause the alarm to be displayed on a display component to be    modified.

-   Embodiment 40. A system according to embodiment 39, wherein the    alarm is modified by being suppressed during the period of    inconsistency.

-   Embodiment 41. A system according to embodiment 39, wherein the    alarm is modified by requiring multiple consecutive asystole and/or    atrial fibrillation determinations be identified in order for the    alarm to be displayed on the display component.

-   Embodiment 42. A system according to one of embodiments 32-41,    further comprising an optical sensor configured to be worn on the    patient's body and generate a time-dependent plethysmogram waveform,    wherein the processing component if configured to receive and    process the time-dependent plethysmogram waveform for the patient,

-   wherein if the occurrence of asystole and/or atrial fibrillation is    determined, the processing component is further configured to    determine if the plethysmogram waveform is inconsistent with the    occurrence of asystole and/or atrial fibrillation and, if so, cause    the alarm to be displayed on a display component to be modified.

-   Embodiment 43. A system according to embodiment 42, wherein the    alarm is modified by being suppressed during the period of    inconsistency.

-   Embodiment 44. A system according to embodiment 42, wherein the    alarm is modified by requiring multiple consecutive asystole and/or    atrial fibrillation determinations be identified in order for the    alarm to be displayed on the display component.

-   Embodiment 45. A method for determining the occurrence or    nonoccurrence of ventricular fibrillation/tachycardia, comprising:

-   obtaining a plurality of time-dependent electrocardiogram (ECG)    waveforms from an ECG sensor comprising plurality of ECG electrodes,    each waveform in the plurality of waveforms corresponding to    electrical signals obtained from one ECG electrode in the plurality    of ECG electrodes;

-   processing at least two of the plurality of time-dependent ECG    waveforms by    -   selecting from each of the at least two ECG waveforms, a first        waveform segment of time length t, and a second waveform segment        of time length t, wherein the first and second waveform segments        are non-overlapping consecutive segments, and    -   for each of the first and second waveform segments, calculating        a four-dimensional feature space comprising at least one        temporal feature, at least one spectral feature, and at least        one a complexity feature;

-   for each of the at least two ECG waveforms, determining if the    four-dimensional feature space is indicative of the occurrence of    ventricular fibrillation/tachycardia, wherein if ventricular    fibrillation/tachycardia is indicated by processing of each of the    at least two ECG waveforms, the occurrence ventricular    fibrillation/tachycardia is determined; and

-   cause an alarm to be displayed on a display component when    ventricular fibrillation/tachycardia is determined to occur.

-   Embodiment 46. A method according to embodiment 45, wherein the    four-dimensional feature space comprises threshold crossing sample    count (TCSC), VF filter (VFleak), sample entropy, and Count2    features.

-   Embodiment 47. A method according to one of embodiments 45 or 46,    further comprising

-   determining an activity type or activity level for the patient using    time-dependent motion waveforms obtained from one or more    accelerometers worn on the patient's body, and if the occurrence of    ventricular fibrillation/tachycardia is determined,    -   determining if the activity type or activity level is        inconsistent with the occurrence of ventricular        fibrillation/tachycardia and, if so, causing the alarm to be        displayed on a display component to be modified.

-   Embodiment 48. A method according to embodiment 47, wherein the    alarm is modified by being suppressed during the period of    inconsistency.

-   Embodiment 49. A method according to embodiment 47, wherein the    alarm is modified by requiring multiple consecutive asystole and/or    atrial fibrillation determinations be identified in order for the    alarm to be displayed on the display component.

-   Embodiment 50. A method according to one of embodiments 45-49,    further comprising

-   determining a photoplethysmogram for the patient using    time-dependent optical waveforms obtained from an optical sensor    worn on the patient's body, and if the occurrence of ventricular    fibrillation/tachycardia is determined,    -   determining if the photoplethysmogram is inconsistent with the        occurrence of ventricular fibrillation/tachycardia and, if so,        causing the alarm to be displayed on a display component to be        modified.

-   Embodiment 51. A method according to embodiment 50, wherein the    alarm is modified by being suppressed during the period of    inconsistency.

-   Embodiment 52. A method according to embodiment 50, wherein the    alarm is modified by requiring multiple consecutive asystole and/or    atrial fibrillation determinations be identified in order for the    alarm to be displayed on the display component.

-   Embodiment 53. A system for continuously monitoring a patient for    cardiac electrical abnormalities, comprising:

an ECG sensor comprising plurality of ECG electrodes configured to beworn on the patient's body, the sensor configured to generate aplurality of time-dependent ECG waveforms, each waveform in theplurality of waveforms corresponding to electrical signals obtained fromone ECG electrode in the plurality of ECG electrodes;

a processing component configured to receive and process the pluralityof time-dependent ECG waveforms by

-   -   selecting from each of the at least two ECG waveforms, a first        waveform segment of time length t, and a second waveform segment        of time length t, wherein the first and second waveform segments        are non-overlapping consecutive segments, and    -   for each of the first and second waveform segments, calculating        a four-dimensional feature space comprising at least one        temporal feature, at least one spectral feature, and at least        one a complexity feature;

-   for each of the at least two ECG waveforms, determining if the    four-dimensional feature space is indicative of the occurrence of    ventricular fibrillation/tachycardia, wherein if ventricular    fibrillation/tachycardia is indicated by processing of each of the    at least two ECG waveforms, the occurrence ventricular    fibrillation/tachycardia is determined; and

-   cause an alarm to be displayed on a display component when    ventricular fibrillation/tachycardia is determined to occur.

-   Embodiment 54. A system according to embodiment 53, wherein the    four-dimensional feature space comprises threshold crossing sample    count (TCSC), VF filter (VFleak), sample entropy, and Count2    features.

-   Embodiment 55. A system according to one of embodiments 53 or 54,    further comprising one or more accelerometers configured to be worn    on the patient's body and generate one or more time-dependent motion    waveforms indicative of patient motion, wherein the processing    component if configured to receive and process the one or more    time-dependent motion waveforms to determine an activity type or    activity level for the patient,

-   wherein if the occurrence of asystole and/or atrial fibrillation is    determined, the processing component is further configured to    determine if the activity type or activity level is inconsistent    with the occurrence of asystole and/or atrial fibrillation and, if    so, cause the alarm to be displayed on a display component to be    modified.

-   Embodiment 56. A system according to embodiment 55, wherein the    alarm is modified by being suppressed during the period of    inconsistency.

-   Embodiment 57. A system according to embodiment 55, wherein the    alarm is modified by requiring multiple consecutive asystole and/or    atrial fibrillation determinations be identified in order for the    alarm to be displayed on the display component.

-   Embodiment 58. A system according to one of embodiments 53-57,    further comprising an optical sensor configured to be worn on the    patient's body and generate a time-dependent plethysmogram waveform,    wherein the processing component if configured to receive and    process the time-dependent plethysmogram waveform for the patient,

-   wherein if the occurrence of asystole and/or atrial fibrillation is    determined, the processing component is further configured to    determine if the plethysmogram waveform is inconsistent with the    occurrence of asystole and/or atrial fibrillation and, if so, cause    the alarm to be displayed on a display component to be modified.

-   Embodiment 59. A system according to embodiment 58, wherein the    alarm is modified by being suppressed during the period of    inconsistency.

-   Embodiment 60. A system according to embodiment 58, wherein the    alarm is modified by requiring multiple consecutive asystole and/or    atrial fibrillation determinations be identified in order for the    alarm to be displayed on the display component.

The following references are incorporated by reference in theirentirety.

-   Tabakov S, Iliev I, Krasteva V. Online Digital Filter and QRS    Detector Applicable in Low Resource ECG Monitoring Systems. Annals    of Biomedical Engineering; Vol. 36, No. 11, 2008:1805-1815-   Dash S, Chon K H, Lu S, Raeder E A. Automatic Real Time Detection of    Atrial Fibrillation. Annals of Biomedical Engineering; Vol. 37, No.    9, 2009:1701-1709.-   Alcaraz R, Rieta J J. A review on sample entropy applications of the    non-invasive analysis of atrial fibrillation electrocardiograms.    Biomedical Signal Processing and Control; Vol. 5, 2010:1-14.-   Li Q, Rajagopalan C, Clifford G. Ventricular Fibrillation and    Tachycardia Classification Using a Machine Learning Approach. IEEE    Transactions on Biomedical Engineering; Vol. 61, No 6, June    2014:1607-1613.-   Jekova I, Krasteva V. Real Time detection of ventricular    fibrillation and tachycardia. Physiologic Measurement; Vol. 25,    2004: 1167-1178.-   Arafat M A, Chowdhurry A W, Hasan K. A simple time domain algorithm    for detection of ventricular fibrillation in electrocardiogram.    Signal, Image, and Video Processing; Vol. 5, 2011: 1-10.-   Jekova I. Shock advisory tool: Detection of life threatening cardiac    arrhythmias and shock success prediction by means of a common    parameter set. Biomedical Signal Processing and Control; Vol. 2,    2007: 25-33.-   Li H, Han W, Hu C, Max Q. Detecting Ventricular Fibrillation by Fast    Algorithm of Dynamic Sample Entropy. Proceedings of the 2009 IEEE    International Conference on Robotics and Biomimetics; 2009:    1105-1110.-   Zhu J, Rosset S, Zou H, Hastie T. Multi-class AdaBoost. Statistics    And Its Interface; Vol. 2, 2009: 349-360.

While the invention has been described and exemplified in sufficientdetail for those skilled in this art to make and use it, variousalternatives, modifications, and improvements should be apparent withoutdeparting from the spirit and scope of the invention. The examplesprovided herein are representative of preferred embodiments, areexemplary, and are not intended as limitations on the scope of theinvention. Modifications therein and other uses will occur to thoseskilled in the art. These modifications are encompassed within thespirit of the invention and are defined by the scope of the claims.

It will be readily apparent to a person skilled in the art that varyingsubstitutions and modifications may be made to the invention disclosedherein without departing from the scope and spirit of the invention.

All patents and publications mentioned in the specification areindicative of the levels of those of ordinary skill in the art to whichthe invention pertains. All patents and publications are hereinincorporated by reference to the same extent as if each individualpublication was specifically and individually indicated to beincorporated by reference.

The invention illustratively described herein suitably may be practicedin the absence of any element or elements, limitation or limitationswhich is not specifically disclosed herein. Thus, for example, in eachinstance herein any of the terms “comprising”, “consisting essentiallyof” and “consisting of” may be replaced with either of the other twoterms. The terms and expressions which have been employed are used asterms of description and not of limitation, and there is no intentionthat in the use of such terms and expressions of excluding anyequivalents of the features shown and described or portions thereof, butit is recognized that various modifications are possible within thescope of the invention claimed. Thus, it should be understood thatalthough the present invention has been specifically disclosed bypreferred embodiments and optional features, modification and variationof the concepts herein disclosed may be resorted to by those skilled inthe art, and that such modifications and variations are considered to bewithin the scope of this invention as defined by the appended claims.

Other embodiments are set forth within the following claims.

We claim:
 1. A method for continuously monitoring a patient for cardiac electrical abnormalities, comprising: obtaining a plurality of time-dependent electrocardiogram (ECG) waveforms from an ECG sensor comprising plurality of ECG electrodes, each waveform in the plurality of waveforms corresponding to electrical signals obtained from one ECG electrode in the plurality of ECG electrodes; processing the plurality of waveforms by determining a time-dependent first signal quality parameter for each waveform in the plurality of waveforms and curating the plurality of waveforms by comparing each first signal quality parameter to a first quality threshold metric, wherein if at least one first signal quality parameter exceeds the first quality threshold metric, accepting those waveforms having a first signal quality parameter that exceed the first quality threshold metric and discarding those waveforms having a first signal quality parameter that does not exceed the first quality threshold metric, or if no first signal quality parameter exceeds the first quality threshold metric, accepting all waveforms, and combining the accepted waveforms to provide a time-dependent combined ECG waveform; processing the combined ECG waveform to by identifying each QRS complex in the combined ECG waveform, determining a second signal quality parameter for each QRS complex by gravity cliff detection, and curating each second signal quality parameter by comparing each second signal quality parameter to a second quality threshold metric, wherein if the second signal quality parameter exceeds the second quality threshold metric, the QRS complex is identified as a valid QRS complex; determining the occurrence or nonoccurrence of asystole and/or atrial fibrillation from the valid QRS complexes; and causing an alarm to be displayed on a display component when asystole and/or atrial fibrillation is determined to occur.
 2. A method according to claim 1, wherein the method further comprises determining the occurrence or nonoccurrence of ventricular fibrillation/tachycardia by processing at least two of the plurality of time-dependent ECG waveforms by selecting from each of the at least two ECG waveforms, a first waveform segment of time length t, and a second waveform segment of time length t, wherein the first and second waveform segments are non-overlapping consecutive segments, and for each of the first and second waveform segments, calculating a four-dimensional feature space comprising at least one temporal feature, at least one spectral feature, and at least one a complexity feature; for each of the at least two ECG waveforms, determining if the four-dimensional feature space is indicative of the occurrence of ventricular fibrillation/tachycardia, wherein if ventricular fibrillation/tachycardia is indicated by processing of each of the at least two ECG waveforms, the occurrence ventricular fibrillation/tachycardia is determined; and cause an alarm to be displayed on a display component when ventricular fibrillation/tachycardia is determined to occur.
 3. A method according to claim 2, further comprising determining an activity type or activity level for the patient using time-dependent motion waveforms obtained from one or more accelerometers worn on the patient's body, and if the occurrence of ventricular fibrillation/tachycardia is determined, determining if the activity type or activity level is inconsistent with the occurrence of ventricular fibrillation/tachycardia and, if so, causing the alarm to be displayed on a display component to be modified.
 4. A method according to claim 3, wherein the alarm is modified by being suppressed during the period of inconsistency.
 5. A method according to claim 3, wherein the alarm is modified by requiring multiple consecutive asystole and/or atrial fibrillation determinations be identified in order for the alarm to be displayed on the display component.
 6. A method according to claim 2, further comprising determining a photoplethysmogram for the patient using time-dependent optical waveforms obtained from an optical sensor worn on the patient's body, and if the occurrence of ventricular fibrillation/tachycardia is determined, determining if the photoplethysmogram is inconsistent with the occurrence of ventricular fibrillation/tachycardia and, if so, causing the alarm to be displayed on a display component to be modified.
 7. A method according to claim 6, wherein the alarm is modified by being suppressed during the period of inconsistency.
 8. A method according to claim 6, wherein the alarm is modified by requiring multiple consecutive asystole and/or atrial fibrillation determinations be identified in order for the alarm to be displayed on the display component.
 9. A method according to claim 2, wherein the four-dimensional feature space comprises threshold crossing sample count (TCSC), VF filter (VFleak), sample entropy, and Count2 features.
 10. A method according to claim 1, wherein the first signal quality parameter is a kurtosis value calculated for each waveform in the plurality of waveforms.
 11. A method according to claim 10, wherein the kurtosis value for each waveform in the plurality of waveforms is calculated from a time window of a predetermined length in each waveform.
 12. The method of claim 11, wherein the kurtosis value for each waveform is updated at an interval of between 2 and 20 seconds, and preferably about every 3 to about every 5 seconds.
 13. A method according to claim 10, wherein the second signal quality parameter is determined using a cliff amplitude and an elapsed time since the previous valid QRS complex identified.
 14. A method according to claim 1, wherein the method comprises determining the occurrence or nonoccurrence of atrial fibrillation, wherein atrial fibrillation is determined by, for a plurality of pairs of consecutive valid QRS complexes occurring over a predetermined time period, for each consecutive pair of valid QRS complexes, determining an interval between a first fiducial point in the first member of the consecutive pair to a corresponding fiducial point in the first member of the consecutive pair, thereby providing a plurality of intervals, curating the plurality of intervals by calculating a third signal quality parameter for each interval and comparing each third signal quality parameter to a third quality threshold metric, wherein if the third signal quality parameter exceeds the third quality threshold metric, the interval is identified as a valid interval, and classifying whether the valid intervals obtained from the plurality of pairs of consecutive valid QRS complexes are indicative of atrial fibrillation.
 15. A method according to claim 14, wherein the classifying step comprises calculating a root mean square of successive differences in the valid intervals.
 16. A method according to claim 14, wherein the classifying step comprises calculating a sample entropy of successive differences in the valid intervals.
 17. A method according to claim 14, wherein the classifying step comprises calculating a two dimensional space that is a function of a root mean square of successive differences in the valid intervals and a sample entropy of successive differences in the valid intervals, and defining values that fall within an area within the two dimensional space as being indicative of the occurrence of atrial fibrillation.
 18. A method according to claim 1, further comprising determining an activity type or activity level for the patient using time-dependent motion waveforms obtained from one or more accelerometers worn on the patient's body, and if the occurrence of asystole and/or atrial fibrillation is determined, determining if the activity type or activity level is inconsistent with the occurrence of asystole and/or atrial fibrillation and, if so, causing the alarm to be displayed on a display component to be modified.
 19. A method according to claim 18, wherein the alarm is modified by being suppressed during the period of inconsistency.
 20. A method according to claim 18, wherein the alarm is modified by requiring multiple consecutive asystole and/or atrial fibrillation determinations be identified in order for the alarm to be displayed on the display component.
 21. A method according to claim 1, further comprising determining a photoplethysmogram for the patient using time-dependent optical waveforms obtained from an optical sensor worn on the patient's body, and if the occurrence of asystole and/or atrial fibrillation is determined, determining if the photoplethysmogram is inconsistent with the occurrence of asystole and/or atrial fibrillation and, if so, causing the alarm to be displayed on a display component to be modified.
 22. A method according to claim 21, wherein the alarm is modified by being suppressed during the period of inconsistency.
 23. A method according to claim 21, wherein the alarm is modified by requiring multiple consecutive asystole and/or atrial fibrillation determinations be identified in order for the alarm to be displayed on the display component.
 24. A method according to claim 1, wherein the method comprises determining the occurrence or nonoccurrence of asystole, wherein asystole is determined to occur when no valid QRS complexes are identified over a predetermined time period.
 25. A method according to claim 1, wherein each QRS complex in the combined ECG waveform is determined using a Pan-Tompkins algorithm.
 26. A system for continuously monitoring a patient for cardiac electrical abnormalities, comprising: an ECG sensor comprising plurality of ECG electrodes configured to be worn on the patient's body, the sensor configured to generate a plurality of time-dependent ECG waveforms, each waveform in the plurality of waveforms corresponding to electrical signals obtained from one ECG electrode in the plurality of ECG electrodes; a processing component configured to receive and process the plurality of time-dependent ECG waveforms by determining a time-dependent first signal quality parameter for each waveform in the plurality of waveforms and curating the plurality of waveforms by comparing each first signal quality parameter to a first quality threshold metric, wherein if at least one first signal quality parameter exceeds the first quality threshold metric, accepting those waveforms having a first signal quality parameter that exceed the first quality threshold metric and discarding those waveforms having a first signal quality parameter that does not exceed the first quality threshold metric, or if no first signal quality parameter exceeds the first quality threshold metric, accepting all waveforms, and combining the accepted waveforms to provide a time-dependent combined ECG waveform; the processing component further configured to process the combined ECG waveform to by identifying each QRS complex in the combined ECG waveform, determining a second signal quality parameter for each QRS complex by gravity cliff detection, curating each second signal quality parameter by comparing each second signal quality parameter to a second quality threshold metric, wherein if the second signal quality parameter exceeds the second quality threshold metric, the QRS complex is identified as a valid QRS complex; determining the occurrence or nonoccurrence of asystole and/or atrial fibrillation from the valid QRS complexes; and cause an alarm to be displayed on a display component when asystole and/or atrial fibrillation is determined to occur. 